Overview: Lipid Metabolism in the Tumor Microenvironment

Adherence to the low-fat diet pattern reduces the risk of lung cancer in American adults aged 55 years and above: a prospective cohort study

OBJECTIVES

There is little evidence on the association between low-fat dietary patterns and lung cancer risk among middle-aged and older adults. To fill this gap, we comprehensively investigated the association of adherence to a low-fat diet (LFD) and intake of different fat components including saturated, monounsaturated, and polyunsaturated fatty acids with incidence of lung cancer and its subtypes [non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC)] among adults aged 55 years and older.

DESIGN

A prospective cohort study with a mean follow-up time of 8.8 years.

SETTING AND PARTICIPANTS

This study used data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. The study population included 98,459 PLCO participants age 55 and over at baseline who completed food frequency questionnaires providing detailed dietary information and had no history of cancer.

METHODS

Dietary intake was assessed using a validated food frequency questionnaire at baseline. A LFD score was calculated based on fat, protein, and carbohydrate intake as a percentage of total calories. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between LFD score and intake of fat components (in quartiles) and incident lung cancer and its subtypes over follow-up. Restricted cubic spline analyses were conducted to examine possible nonlinear relationships. Subgroup analyses were performed to evaluate potential effect modifiers, and several sensitivity analyses were conducted to assess the stability of the findings.

RESULTS

During a follow-up of 869,807.9 person-years, 1,642 cases of lung cancer were observed, consisting of 1,408 (85.75%) cases of NSCLC and 234 (14.25%) cases of SCLC. The highest versus the lowest quartiles of the LFD score were found to be associated with a reduced risk of lung cancer (HR, 0.76; 95% CI, 0.66-0.89), NSCLC (HR, 0.79; 95% CI, 0.67-0.93), and SCLC (HR, 0.59; 95% CI, 0.38-0.92). The restricted cubic spline plots demonstrated a linear dose-response relationship between the LFD score and the risk of lung cancer as well as its subtypes. This risk reduction association for overall lung cancer was more pronounced in smokers (HR, 0.71; 95% CI, 0.60-0.84; P for interaction = 0.003). For fat components, high consumption of saturated fatty acids was associated with an increased lung cancer risk (HR, 1.35; 95% CI, 1.10-1.66), especially for SCLC (HR, 2.05; 95% CI, 1.20-3.53). No significant association was found between consumption of monounsaturated or polyunsaturated fatty acids and incident lung cancer and its subtypes.

CONCLUSIONS

Our findings suggest that adherence to LFD may reduce the lung cancer risk, particularly in smokers; while high saturated fatty acids consumption may increase lung cancer risk, especially for SCLC, among middle-aged and older adults in the US population.

Landscape of tumoral ecosystem for enhanced anti-PD-1 immunotherapy by gut Akkermansia muciniphila

Gut Akkermansia muciniphila (Akk) has been implicated in impacting immunotherapy or oncogenesis. This study aims to dissect the Akk-associated tumor immune ecosystem (TIME) by single-cell profiling coupled with T cell receptor (TCR) sequencing. We adopted mouse cancer models under anti-PD-1 immunotherapy, combined with oral administration of three forms of Akk, including live Akk, pasteurized Akk (Akk-past), or its membrane protein Amuc_1100 (Amuc). We show that live Akk is most effective in activation of CD8 T cells by rescuing the exhausted type into cytotoxic subpopulations. Remarkably, only live Akk activates MHC-II-pDC pathways, downregulates CXCL3 in Bgn(+)Dcn(+) cancer-associated fibroblasts (CAFs), blunts crosstalk between Bgn(+)Dcn(+) CAFs and PD-L1(+) neutrophils by a CXCL3-PD-L1 axis, and further suppresses the crosstalk between PD-L1(+) neutrophils and CD8 T cells, leading to the rescue of exhausted CD8 T cells. Together, this comprehensive picture of the tumor ecosystem provides deeper insights into immune mechanisms associated with gut Akk-dependent anti-PD-1 immunotherapy.

Advances in reprogramming of energy metabolism in tumor T cells

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Study on the mechanism of MDSC-platelets and their role in the breast cancer microenvironment

Myeloid-derived suppressor cells (MDSCs) are key immunosuppressive cells in the tumor microenvironment (TME) that play critical roles in promoting tumor growth and metastasis. Tumor-associated platelets (TAPs) help cancer cells evade the immune system and promote metastasis. In this paper, we describe the interaction between MDSCs and TAPs, including their generation, secretion, activation, and recruitment, as well as the effects of MDSCs and platelets on the generation and changes in the immune, metabolic, and angiogenic breast cancer (BC) microenvironments. In addition, we summarize preclinical and clinical studies, traditional Chinese medicine (TCM) therapeutic approaches, and new technologies related to targeting and preventing MDSCs from interacting with TAPs to modulate the BC TME, discuss the potential mechanisms, and provide perspectives for future development. The therapeutic strategies discussed in this review may have implications in promoting the normalization of the BC TME, reducing primary tumor growth and distant lung metastasis, and improving the efficiency of anti-tumor therapy, thereby improving the overall survival (OS) and progression-free survival (PFS) of patients. However, despite the significant advances in understanding these mechanisms and therapeutic strategies, the complexity and heterogeneity of MDSCs and side effects of antiplatelet agents remain challenging. This requires further investigation in future prospective cohort studies.

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

BACKGROUND

The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

METHODS

Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

RESULTS

Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

CONCLUSION

The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.

Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy

Tumor metabolism and tumor immunity are inextricably linked. Targeting the metabolism of tumors is a point worth studying in tumor immunotherapy. Recently, the influence of the metabolism of tumors and immune cells on the occurrence, proliferation, metastasis, and prognosis of tumors has attracted more attention. Tumor tissue forms a specific tumor microenvironment (TME). In addition to tumor cells, there are also immune cells, stromal cells, and other cells in TME. To adapt to the environment, tumor cells go through the metabolism reprogramming of various substances. The metabolism reprogramming of tumor cells may further affect the formation of the tumor microenvironment and the function of a variety of cells, especially immune cells, eventually promoting tumor development. Therefore, it is necessary to study the metabolism of tumor cells and its effects on immune cells to guide tumor immunotherapy. Inhibiting tumor metabolism may restore immune balance and promote the immune response in tumors. This article will describe glucose metabolism, lipid metabolism, amino acid metabolism, and immune cells in tumors. Besides, the impact of metabolism on the immune cells in TME is also discussed for analyzing and exploring tumor immunotherapy.

Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features

Clear cell renal cell carcinoma (ccRCC) is a major pathological type of kidney cancer with a poor prognosis due to a lack of biomarkers for early diagnosis and prognosis prediction of ccRCC. In this study, we investigated the aberrant expression of Acyl-coenzyme A oxidase 1 (ACOX1) in ccRCC and evaluated its potential in diagnosis and prognosis. ACOX1 is the first rate-limiting enzyme in the peroxidation β-oxidation pathway and is involved in the regulation of fatty acid oxidative catabolism. The mRNA and protein levels of ACOX1 were significantly downregulated in ccRCC, and its downregulation was closely associated with the tumor-node-metastasis stage of patients. The ROC curves showed that ACOX1 possesses a high diagnostic value for ccRCC. The OS analysis suggested that lower expression of ACOX1 was closely related to the worse outcome of patients. In addition, gene set enrichment analysis suggested that expression of ACOX1 was positively correlated with CDH1, CDH2, CDKL2, and EPCAM, while negatively correlated with MMP9 and VIM, which strongly indicated that ACOX1 may inhibit the invasion and migration of ccRCC by reversing epithelial-mesenchymal transition. Furthermore, we screened out that miR-16-5p is upregulated at the mRNA transcript level in ccRCC and negatively correlated with ACOX1. In conclusion, our results showed that ACOX1 is abnormally low expressed in ccRCC, suggesting that it could serve as a diagnostic and prognostic biomarker for ccRCC. Overexpression of miR-16-5p may be responsible for the inactivation of ACOX1.

Lipid metabolism in malignant tumor brain metastasis: reprogramming and therapeutic potential

INTRODUCTION

Brain metastasis is a highly traumatic event in the progression of malignant tumors, often symbolizing higher mortality. Metabolic alterations are hallmarks of cancer, and the mask of lipid metabolic program rearrangement in cancer progression is gradually being unraveled.

AREAS COVERED

In this work, we reviewed clinical and fundamental studies related to lipid expression and activity changes in brain metastases originating from lung, breast, and cutaneous melanomas, respectively. Novel roles of lipid metabolic reprogramming in the development of brain metastasis from malignant tumors were identified and its potential as a therapeutic target was evaluated. Published literature and clinical studies in databases consisting of PubMed, Embase, Scopus and www.ClinicalTrials.gov from 1990 to 2022 were searched.

EXPERT OPINION

Lipid metabolic reprogramming in brain metastasis is involved in de novo lipid synthesis within low lipid availability environments, regulation of lipid uptake and storage, metabolic interactions between brain tumors and the brain microenvironment, and membrane lipid remodeling, in addition to being a second messenger for signal transduction. Although some lipid metabolism modulators work efficiently in preclinical models, there is still a long way to go from laboratory to clinic. This area of research holds assurance for the organ-targeted treatment of brain metastases through drug-regulated metabolic targets and dietary interventions.

Targeting cancer metabolic vulnerabilities for advanced therapeutic efficacy

Cancer metabolism is how cancer cells utilize nutrients and energy to support their growth and proliferation. Unlike normal cells, cancer cells have a unique metabolic profile that allows them to generate energy and the building blocks they need for rapid growth and division. This metabolic profile is marked by an increased reliance on glucose and glutamine as energy sources and changes in how cancer cells use and make key metabolic intermediates like ATP, NADH, and NADPH. This script analyzes a comprehensive overview of the latest advances in tumor metabolism, identifying the key unresolved issues, elaborates on how tumor cells differ from normal cells in their metabolism of nutrients, and explains how tumor cells conflate growth signals and nutrients to proliferate. The metabolic interaction of tumorigenesis and lipid metabolism within the tumor microenvironment and the role of ROS as an anti-tumor agent by mediating various signaling pathways for clinical cancer therapeutic targeting are outlined. Cancer metabolism is highly dynamic and heterogeneous; thus, advanced technologies to better investigate metabolism at the unicellular level without altering tumor tissue are necessary for better research and clinical transformation. The study of cancer metabolism is an area of active research, as scientists seek to understand the underlying metabolic changes that drive cancer growth and to identify potential therapeutic targets.

Functional states of myeloid cells in cancer

Myeloid cells, comprised of macrophages, dendritic cells, monocytes, and granulocytes, represent a major component of the tumor microenvironment (TME) and are critically involved in regulation of tumor progression and metastasis. In recent years, single-cell omics technologies have identified multiple phenotypically distinct subpopulations. In this review, we discuss recent data and concepts suggesting that the biology of myeloid cells is largely defined by a very limited number of functional states that transcend the narrowly defined cell populations. These functional states are primarily centered around classical and pathological states of activation, with the latter state commonly defined as myeloid-derived suppressor cells. We discuss the concept that lipid peroxidation of myeloid cells represents a major mechanism that governs their pathological state of activation in the TME. Lipid peroxidation is associated with ferroptosis mediating suppressive activity of these cells and thus could be considered an attractive target for therapeutic intervention.

Metabolic-associated signature and hub genes associated with immune microenvironment and prognosis in bladder cancer

The relationship between metabolism and immune microenvironment remains to be studied in bladder cancer (BCa). We aimed to construct a metabolic-associated signature for prognostic prediction and investigate its relationship with the immune microenvironment in BCa. The RNA expression of metabolism associated genes was obtained from a combined data set including The Cancer Genome Atlas, GSE48075, and GSE13507 to divide BCa patients into different clusters. A metabolic-associated signature was constructed using the differentially expressed genes between clusters in the combined data set and validated in the IMvigor210 trial and our center. The composition of tumor-infiltrating immune cells (TIICs) was evaluated using the single-sample Gene Set Variation Analysis. BCa patients in Cluster A or high-risk level were associated with advanced clinicopathological features and poor survival outcomes. The percentage of high-risk patients was significantly lower in patients responding to anti-PD-L1 treatment. Compared with low-risk patients, the IC50 values of cisplatin and gemcitabine were significantly lower in high-risk patients. Thiosulfate transferase (TST) and S100A16 were significantly associated with clinicopathological features and prognosis. Downregulation of TST promoted BCa cell invasion, migration, and epithelial-to-mesenchymal transition, which are inhibited by downregulation of S100A16. CD8 + T cells, neutrophils, and dendritic cells had higher infiltration in the TST low-level and the S100A16 high-level. Furthermore, loss of function TST and S100A16 significantly affected the expression of PD-L1 and CD47. The metabolic-associated signature can stratify BCa patients into distinct risk levels with different immunotherapeutic susceptibility and survival outcomes. Metabolism disorder promoted the dysregulation of immune microenvironment, thus contributing to immunosuppression.

Lipid metabolism-related gene signature predicts prognosis and depicts tumor microenvironment immune landscape in gliomas

Background

Glioma is the most common primary brain tumor in adults and accounts for more than 70% of brain malignancies. Lipids are crucial components of biological membranes and other structures in cells. Accumulating evidence has supported the role of lipid metabolism in reshaping the tumor immune microenvironment (TME). However, the relationship between the immune TME of glioma and lipid metabolism remain poorly described.

Materials and methods

The RNA-seq data and clinicopathological information of primary glioma patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). An independent RNA-seq dataset from the West China Hospital (WCH) also included in the study. Univariate Cox regression and LASSO Cox regression model was first to determine the prognostic gene signature from lipid metabolism-related genes (LMRGs). Then a risk score named LMRGs-related risk score (LRS) was established and patients were stratified into high and low risk groups according to LRS. The prognostic value of the LRS was further demonstrated by construction of a glioma risk nomogram. ESTIMATE and CIBERSORTx were used to depicted the TME immune landscape. Tumor Immune Dysfunction and Exclusion (TIDE) was utilized to predict the therapeutic response of immune checkpoint blockades (ICB) among glioma patients.

Results

A total of 144 LMRGs were differentially expressed between gliomas and brain tissue. Finally, 11 prognostic LMRGs were included in the construction of LRS. The LRS was demonstrated to be an independent prognostic predictor for glioma patients, and a nomogram consisting of the LRS, IDH mutational status, WHO grade, and radiotherapy showed a C-index of 0.852. LRS values were significantly associated with stromal score, immune score, and ESTIMATE score. CIBERSORTx indicated remarkable differences in the abundance of TME immune cells between patients with high and low LRS risk levels. Based on the results of TIDE algorithm, we speculated that the high-risk group had a greater chance of benefiting from immunotherapy.

Conclusion

The risk model based upon LMRGs could effectively predict prognosis in patients with glioma. Risk score also divided glioma patients into different groups with distinct TME immune characteristics. Immunotherapy is potentially beneficial to glioma patients with certain lipid metabolism profiles.

Investigating the effects and mechanisms of Erchen Decoction in the treatment of colorectal cancer by network pharmacology and experimental validation

Objective: Erchen Decoction (ECD), a well-known traditional Chinese medicine, exerts metabolism-regulatory, immunoregulation, and anti-tumor effects. However, the action and pharmacological mechanism of ECD remain largely unclear. In the present study, we explored the effects and mechanisms of ECD in the treatment of CRC using network pharmacology, molecular docking, and systematic experimental validation.

Methods: The active components of ECD were obtained from the TCMSP database and the potential targets of them were annotated by the STRING database. The CRC-related targets were identified from different databases (OMIM, DisGeNet, GeneCards, and DrugBank). The interactive targets of ECD and CRC were screened and the protein-protein interaction (PPI) networks were constructed. Then, the hub interactive targets were calculated and visualized from the PPI network using the Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. In addition, the molecular docking was performed. Finally, systematic in vitro, in vivo and molecular biology experiments were performed to further explore the anti-tumor effects and underlying mechanisms of ECD in CRC.

Results: A total of 116 active components and 246 targets of ECD were predicted based on the component-target network analysis. 2406 CRC-related targets were obtained from different databases and 140 intersective targets were identified between ECD and CRC. 12 hub molecules (STAT3, JUN, MAPK3, TP53, MAPK1, RELA, FOS, ESR1, IL6, MAPK14, MYC, and CDKN1A) were finally screened from PPI network. GO and KEGG pathway enrichment analyses demonstrated that the biological discrepancy was mainly focused on the tumorigenesis-, immune-, and mechanism-related pathways. Based on the experimental validation, ECD could suppress the proliferation of CRC cells by inhibiting cell cycle and promoting cell apoptosis. In addition, ECD could inhibit tumor growth in mice. Finally, the results of molecular biology experiments suggested ECD could regulate the transcriptional levels of several hub molecules during the development of CRC, including MAPKs, PPARs, TP53, and STATs.

Conclusion: This study revealed the potential pharmacodynamic material basis and underlying molecular mechanisms of ECD in the treatment of CRC, providing a novel insight for us to find more effective anti-CRC drugs.

Development and validation of a screening model for lung cancer using machine learning: A large-scale, multi-center study of biomarkers in breath

Objectives

Lung cancer (LC) is the largest single cause of death from cancer worldwide, and the lack of effective screening methods for early detection currently results in unsatisfactory curative treatments. We herein aimed to use breath analysis, a noninvasive and very simple method, to identify and validate biomarkers in breath for the screening of lung cancer.

Materials and methods

We enrolled a total of 2308 participants from two centers for online breath analyses using proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS). The derivation cohort included 1007 patients with primary LC and 1036 healthy controls, and the external validation cohort included 158 LC patients and 107 healthy controls. We used eXtreme Gradient Boosting (XGBoost) to create a panel of predictive features and derived a prediction model to identify LC. The optimal number of features was determined by the greatest area under the receiver‐operating characteristic (ROC) curve (AUC).

Results

Six features were defined as a breath-biomarkers panel for the detection of LC. In the training dataset, the model had an AUC of 0.963 (95% CI, 0.941–0.982), and a sensitivity of 87.1% and specificity of 93.5% at a positivity threshold of 0.5. Our model was tested on the independent validation dataset and achieved an AUC of 0.771 (0.718–0.823), and sensitivity of 67.7% and specificity of 73.0%.

Conclusion

Our results suggested that breath analysis may serve as a valid method in screening lung cancer in a borderline population prior to hospital visits. Although our breath-biomarker panel is noninvasive, quick, and simple to use, it will require further calibration and validation in a prospective study within a primary care setting.

Exploration of hub genes, lipid metabolism, and the immune microenvironment in stomach carcinoma and cholangiocarcinoma

Background

Gastric cancer (GC) is the 5th most common cause of cancer in the world and the 3rd largest cause of cancer-related death. It is usually associated with a variety of cancers, of which cholangiocarcinoma (CCA) combined with GC accounts for about 1.6%. This study sought to examine the hub genes and role of lipid metabolism in the development and diagnosis of GC and CCA.

Methods

To screen potential hub genes, The Cancer Genome Atlas (TCGA) data sets, including the GC (STAD, dataset of GC) and CCA (CHOL, dataset of CCA) data sets, were used to conduct a differentially expressed gene (DEG) analysis and an enrichment analysis of the DEGs. A weighted-gene co-expression network analysis (WGCNA) was conducted to identify the significant gene module and then find the hub genes in the module. To verify the 4 hub genes, we conducted a differentiation analysis of the 4 genes in GC and CCA and found that there were differences. A survival analysis of the hub genes was performed and mutations were mapped. Additionally, tumor immune microenvironment (TIME) and immune analyses were performed to evaluate how lipid metabolism affects the development of GC with CCA.

Results

The principal component analysis showed that both GC and CCA had distinct up-regulated and down-regulated genes, which are involved in a variety of metabolic processes. Upon WGCNA, the turquoise and blue modules were meaningful, and the hub genes were identified from these 2 modules. Four hub genes were identified: amyloid beta precursor protein binding family B member 1 (APBB1), Homo sapiens armadillo repeat containing X-linked 1 (ARMCX1), DAZ interacting zinc finger protein 1 (DZIP1), and methionine sulfoxide reductase B3 (MSRB3). In survival analysis, increased expression of the 4 hub genes was associated with inferior survival outcomes, with variations in all 4 genes. Additionally, we demonstrated that genes related to lipid metabolism had an effect on immune function.

Conclusions

APBB1, ARMCX1, DZIP1, and MSRB3 affect the development of GC and CCA and can be used as biomarkers. The expression of lipid metabolism genes is related to the TIME of patients with GC and CCA.

lncRNA ZFAS1 Promotes HMGCR mRNA Stabilization via Binding U2AF2 to Modulate Pancreatic Carcinoma Lipometabolism

Being one of the most lethal malignant tumors worldwide, pancreatic carcinoma (PC) shows strong invasiveness and high mortality. In tumorigenesis and progression, the role played by long-chain noncoding RNAs (lncRNAs) cannot be ignored. This article mainly probes into the function of lncRNA ZFAS1 in PC. ZFAS1 expression in PC and normal counterparts retrieved from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) database was analysed by GEPIA2. Its expression profile in clinical specimens and human PC cell strains was quantified using qRT-PCR. Measurements of BxPC-3 cell multiplication and invasiveness employed CCK-8, plate clone formation test, and Transwell chamber assay. ZFAS1's impact on lipid content in BxPC-3 cells was detected. RNA pulldown and RIP assays analyzed the interaction of ZFAS1 with U2AF2 and HMGCR in BxPC-3 cells. Finally, the impacts of U2AF2 and HMGCR on the biological behavior of BxPC-3 were observed. ZFAS1 was kept at a high level in PC tissues versus the normal counterparts. ZFAS1 gene knockout remarkably suppressed PC cell multiplication and invasiveness and decreased the contents of free fatty acids, total cholesterol, triglycerides, and phospholipids. Mechanistically, ZFAS1 stabilized HMGCR mRNA through U2AF2, thus increasing HMGCR expression and promoting PC lipid accumulation. Meanwhile, reduced PC cell viability and invasiveness were observed after downregulating U2AF2 and HMGCR. As an oncogene of PC, ZFAS1 can modulate lipometabolism and stabilize HMGCR mRNA expression by binding with U2AF2 in PC, which is a candidate target for PC diagnosis and treatment.

Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing Data to Establish and Validate a Prognostic Model for Patients With Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data.

Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated.

Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them.

Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.

Microenvironmental Factors Modulating Tumor Lipid Metabolism: Paving the Way to Better Antitumoral Therapy

Metabolic reprogramming is one of the emerging hallmarks of cancer and is driven by both the oncogenic mutations and challenging microenvironment. To satisfy the demands of energy and biomass for rapid proliferation, the metabolism of various nutrients in tumor cells undergoes important changes, among which the aberrant lipid metabolism has gained increasing attention in facilitating tumor development and metastasis in the past few years. Obstacles emerged in the aspect of application of targeting lipid metabolism for tumor therapy, due to lacking of comprehensive understanding on its regulating mechanism. Tumor cells closely interact with stromal niche, which highly contributes to metabolic rewiring of critical nutrients in cancer cells. This fact makes the impact of microenvironment on tumor lipid metabolism a topic of renewed interest. Abundant evidence has shown that many factors existing in the tumor microenvironment can rewire multiple signaling pathways and proteins involved in lipid metabolic pathways of cancer cells. Hence in this review, we summarized the recent progress on the understanding of microenvironmental factors regulating tumor lipid metabolism, and discuss the potential of modulating lipid metabolism as an anticancer approach.